Qualitative Research for Interdisciplinary Studies: Multiple Methodologies for Multiple Disciplines
Bibliographic record
Abstract
The intent of research is to improve the lives of individuals and communities. Through qualitative research specifically, we can deepen our understanding of reality and investigate the nuances of how people experience and make meaning from it. Qualitative research, as a scientific approach, enables researchers to examine phenomena from the point of view of those who live it. Qualitative researchers seek to understand individuals’ experiences, perceptions, thoughts, and emotions that are constructed through social rules, cultural patterns shaped by the communities and societies in which they live. Although research objectives differ, qualitative research is defined by a systematic process of researchers' identifying and interrogating their positionality, subjectivities, and influential paradigms. In this introduction readers will note similar methodologies; however, these are presented in the context of multiple disciplines. This volume demonstrates the evolving interdisciplinarity of qualitative research methods that engages scholars in innovative conversation. This editorial offers a peek at the interdisciplinary potential of qualitative research. Such interdisciplinary prowess is found in Volume 16 of New Trends in Qualitative Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.321 | 0.294 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".